>> from Cambridge, Massachusetts. It's the Cube covering M. I. T. Chief Data officer and Information Quality Symposium 2019 Brought to you by Silicon Angle Media >> Welcome back to M I. T. Everybody watching the Cube. The leader in live tech coverage we hear a Day two of the M I t chief data officer information Quality Conference Day Volonte with Paul Dillon. Andy Palmer's here. He's the co founder and CEO of Tamer. Good to see again. It's great to see it actually coming out. So I didn't ask this to Mike. I could kind of infirm from someone's dances. But why did you guys start >> Tamer? >> Well, it really started with an academic project that Mike was doing over at M. I. T. And I was over in of artists at the time. Is the chief get officer over there? And what we really found was that there were a lot of companies really suffering from data mastering as the primary bottleneck in their company did used great new tech like the vertical system that we've built and, you know, automated a lot of their warehousing and such. But the real bottleneck was getting lots of data integrated and mastered really, really >> quickly. Yeah, He took us through the sort of problems with obviously the d. W. In terms of scaling master data management and the scanning problems was Was that really the problem that you were trying to solve? >> Yeah, it really was. And when we started, I mean, it was like, seven years ago, eight years ago, now that we started the company and maybe almost 10 when we started working on the academic project, and at that time, people weren't really thinking are worried about that. They were still kind of digesting big data. A zit was called, but I think what Mike and I kind of felt was going on was that people were gonna get over the big data, Um, and the volume of data. And we're going to start worrying about the variety of the data and how to make the data cleaner and more organized. And, uh, I think I think way called that one pretty much right. Maybe >> we're a little >> bit early, but but I think now variety is the big problem >> with the other thing about your big day. Big data's oftentimes associated with Duke, which was a batch and then you sort of saw the shifter real time and spark was gonna fix all that. And so what are you seeing in terms of the trends in terms of how data is being used to drive almost near real time business decisions. >> You know, Mike and I came out really specifically back in 2007 and declared that we thought, uh, Hadoop and H D f s was going to be far less impactful than other people. >> 07 >> Yeah, Yeah. And Mike Mike actually was really aggressive and saying it was gonna be a disaster. And I think we've finally seen that actually play out of it now that the bloom is off the rose, so to speak. And so they're They're these fundamental things that big companies struggle with in terms of their data and, you know, cleaning it up and organizing it and making it, Iike want. Anybody that's worked at one of these big companies can tell you that the data that they get from most of their internal system sucks plain and simple, and so cleaning up that data, turning it into something it's an asset rather than liability is really what what tamers all about? And it's kind of our mission. We're out there to do this and it sort of pails and compare. Do you think about the amount of money that some of these companies have spent on systems like ASAP on you're like, Yeah, but all the data inside of the systems so bad and so, uh, ugly and unuseful like we're gonna fix that problem. >> So you're you're you're special sauce and machine learning. Where are you applying machine learning most most effectively when >> we apply machine learning to probably the least sexy problem on the planet. There are a lot of companies out there that use machine learning and a I t o do predictive algorithms and all kinds of cool stuff. All we do with machine learning is actually use it to clean up data and organize data. Get it ready for people to use a I I I started in the eye industry back in the late 19 eighties on, you know, really, I learned from the sky. Marvin Minsky and Mark Marvin taught me two things. First was garbage in garbage out. There's no algorithm that's worth anything unless you've got great data, and the 2nd 1 is it's always about the human in the machine working together. And I've really been working on those two same principles most of my career, and Tamer really brings both of those together. Our goal is to prepare data so that it can be used analytically inside of these companies, that it's actually high quality and useful. And the way we do that involves bringing together the machine, mostly these advanced machine learning algorithms with humans, subject matter experts inside of these companies that actually know all the ins and outs and all the intricacies of the data inside of their company. >> So say garbage in garbage out. If you don't have good training data course you're not going good ML model. How much how much upfront work is required. G. I know it was one of your customers and how much time is required to put together on ML model that can deal with 20,000,000 records like that? >> Well, you know, the amazing thing that this happened for us in the last five years, especially is that now we've got we've built enough models from scratch inside of these large global 2000 companies that very rarely do we go into a place where there we don't already have a model that's pre built. That they can use is a starting point. And I think that's the same thing that's happening in modeling in general. If you look a great companies like data robot Andi and even in in the Python community ml live that the accessibility of these modeling tools and the models themselves are actually so they're commoditized. And so most of our models and most of the projects we work on, we've already got a model. That's a starting point. We don't really have to start from scratch. >> You mentioned gonna ta I in the eighties Is that is the notion of a I Is it same as it was in the eighties and now we've just got the tooling, the horsepower, the data to take advantage of it is the concept changed? The >> math is all the same, like, you know, absolutely full stop, like there's really no new math. The two things I think that have changed our first. There's a lot more data that's available now, and, you know, uh, neural nets are a great example, right? in Marvin's things that, you know when you look at Google translate and how aggressively they used neural nets, it was the quantity of data that was available that actually made neural nets work. The second thing that that's that's changed is the cheap availability of Compute that Now the largest supercomputer in the world is available to rent by the minute. And so we've got all this data. You've got all this really cheap compute. And then third thing is what you alluded to earlier. The accessibility of all the math that now it's becoming so simple and easy to apply these math techniques, and they're becoming you know, it's It's almost to the point where the average data scientists not the advance With the average data, scientists can do a practice. Aye, aye. Techniques that 20 years ago required five PhDs. >> It's not surprising that Google, with its new neural net technology, all the search data that it has has been so successful. It's a surprise you that that Amazon with Alexa was able to compete so effectively. >> Oh, I think that I would never underestimate Amazon and their ability to, you know, build great tact. They've done some amazing work. One of my favorite Mike and I actually, one of our favorite examples in the last, uh, three years, they took their red shift system, you know, that competed with with Veronica and they they re implemented it and, you know, as a compiled system and it really runs incredibly fast. I mean, that that feat of engineering, what was truly exceptional >> to hear you say that Because it wasn't Red Shift originally Park. So yeah, that's right, Larry Ellison craps all over Red Shift because it's just open source offer that they just took and repackage. But you're saying they did some major engineering to Oh >> my gosh, yeah, It's like Mike and I both way Never. You know, we always compared par, excelled over tika, and, you know, we always knew we were better in a whole bunch of ways. But this this latest rewrite that they've done this compiled version like it's really good. >> So as a guy has been doing a eye for 30 years now, and it's really seeing it come into its own, a lot of a I project seems right now are sort of low hanging fruit is it's small scale stuff where you see a I in five years what kind of projects are going our bar company's gonna be undertaking and what kind of new applications are gonna come out of this? But >> I think we're at the very beginning of this cycle, and actually there's a lot more potential than has been realized. So I think we are in the pick the low hanging fruit kind of a thing. But some of the potential applications of A I are so much more impactful, especially as we modernize core infrastructure in the enterprise. So the enterprise is sort of living with this huge legacy burden. And we always air encouraging a tamer our customers to think of all their existing legacy systems is just dated generating machines and the faster they can get that data into a state where they can start doing state of the art A. I work on top of it, the better. And so really, you know, you gotta put the legacy burden aside and kind of draw this line in the sand so that as you really get, build their muscles on the A. I side that you can take advantage of that with all the data that they're generating every single day. >> Everything about these data repose. He's Enterprise Data Warehouse. You guys built better with MPP technology. Better data warehouses, the master data management stuff, the top down, you know, Enterprise data models, Dupin in big data, none of them really lived up to their promise, you know? Yeah, it's kind of somewhat unfair toe toe like the MPP guys because you said, Hey, we're just gonna run faster. And you did. But you didn't say you're gonna change the world and all that stuff, right? Where's e d? W? Did Do you feel like this next wave is actually gonna live up to the promise? >> I think the next phase is it's very logical. Like, you know, I know you're talking to Chris Lynch here in a minute, and you know what? They're doing it at scale and at scale and tamer. These companies are all in the same general area. That's kind of related to how do you take all this data and actually prepare it and turn it into something that's consumable really quickly and easily for all of these new data consumers in the enterprise and like so that that's the next logical phase in this process. Now, will this phase be the one that finally sort of meets the high expectations that were set 2030 years ago with enterprise data warehousing? I don't know, but we're certainly getting closer >> to I kind of hoped knockers, and we'll have less to do any other cool stuff that you see out there. That was a technology just >> I'm huge. I'm fanatical right now about health care. I think that the opportunity for health care to be transformed with technology is, you know, almost makes everything else look like chump change. What aspect of health care? Well, I think that the most obvious thing is that now, with the consumer sort of in the driver seat in healthcare, that technology companies that come in and provide consumer driven solutions that meet the needs of patients, regardless of how dysfunctional the health care system is, that's killer stuff. We had a great company here in Boston called Pill Pack was a great example of that where they just build something better for consumers, and it was so popular and so, you know, broadly adopted again again. Eventually, Amazon bought it for $1,000,000,000. But those kinds of things and health care Pill pack is just the beginning. There's lots and lots of those kinds of opportunities. >> Well, it's right. Healthcare's ripe for disruption on, and it hasn't been hit with the digital destruction. And neither is financialservices. Really? Certainly, defenses has not yet another. They're high risk industry, so Absolutely takes longer. Well, Andy, thanks so much for making the time. You know, You gotta run. Yeah. Yeah. Thank you. All right, keep it right. Everybody move back with our next guest right after this short break. You're watching the Cube from M I T c B O Q. Right back.

>> From Cambridge, Massachusetts it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by, SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts, everybody. You're watching theCUBE, the leader in live tech coverage. I'm Dave Vellante with my co-host, Paul Gillan. Chris Lynch, good friend is here CEO, newly minted CEO and AtScale and legend. Good to see you. >> In my own mind. >> In mine too. >> It's great to be here. >> It's awesome, thank you for taking time. I know how busy you are, you're running around like crazy your next big thing. I was excited to hear that you got back into it. I predicted it a while ago you were a very successful venture capitalists but at heart, you're startup guy, aren't ya? >> Yeah 100%, 100%. I couldn't be more thrilled, I feel invigorated. I think I've told you many times, when you've interviewed me and asked me about the transition from being an entrepreneur to being a VC and since it's a PG show, I've got a different analog than the one I usually give you. I used to be a movie star and now I'm an executive producer of movies. Now am back to being a movie star, hopefully. >> yeah well, so you told me when you first became a VC you said, I look for startups that have a 10X impact either 10X value, 10X cost reduction. What was it that attracted you to AtScale? What's the 10X? >> AtScale, addresses $150 billion market problem which is basically bringing traditional BI to the cloud. >> That's the other thing you told me, big markets. >> Yeah, so that's the first thing massive market opportunity. The second is, the innovation component and where the 10X comes we're uniquely qualified to virtualize data into the pipeline and out. So I like to say that we're the bridge between BI and AI and back. We make every BI user, a citizen data scientist and that's a game changer. And that's sort of the new futuristic component of what we do. So one part is steeped in, that $150 billion BI marketplace in a traditional analytics platforms and then the second piece is into you delivering the data, into these BI excuse me, these AI machine learning platforms. >> Do you see that ultimately getting integrated into some kind of larger, data pipeline framework. I mean, maybe it lives in the cloud or maybe on prem, how do you see that evolving over time? >> So I believe that, with AtScale as one single pane of glass, we basically are providing an API, to the data and to the user, one single API. The reason that today we haven't seen the delivery of the promise of big data is because we don't have big data. Fortunate 2000 companies don't have big data. They have lots of data but to me big data means you can have one logical view of that data and get the best data pumped into these models in these tools, and today that's not the case. They're constricted by location they're constricted by vendor they're constricted by whether it's in the cloud or on prem. We eliminate those restrictions. >> The single API, I think is important actually. Because when you look at some of these guys what they're doing with their data pipeline they might have 10 or 15 unique API's that they're trying to manage. So there's a simplification aspect to, I suppose. >> One of the knocks on traditional BI has always been the need for extract databases and all the ETL that goes that's involved in that. Do you guys avoid that stage? You go to the production data directly or what's the-- >> It's a great question. The way I put it is, we bring Moses to the mountain the mountain being the data, Moses being the user. Traditionally, what people have been trying to do is bring the mountain to Moses, doesn't scale. At AtScale, we provide an abstraction a logical distraction between the data and the BI user. >> You don't touch, you don't move the data. >> We don't move the data. Which is what's unique and that's what's delivering I think, way more than a 10X delivery in value. >> Because you leave the data in place you bring that value to wherever the data is. Which is the original concept of Hadoop, by the way. That was what was profound about Hadoop everybody craps on it now, but that was the game changer and if you could take advantage of that that's how you tap your 10X. >> To the difference is, we're not, to your point we're not moving the data. Hadoop, in my humble opinion why it plateaued is because to get the value, you had to ask the user to bring and put data in yet another platform. And the reason that we're not delivering on big data as an industry, I believe is because we've too many data sources, too many platforms too many consumers of data and too many producers. As we build all these islands of data, with no connectivity. The idea is, we'll create this big data lake and we're going to physically put everything in there. Guess what? Someday turned out to be never. Because people aren't going to deal with the business disruption. We move thousands of users from a platform like Teradata to a platform like Snowflake or Google BigQuery, we don't care. We're a multi-cloud and we're a hybrid cloud. But we do it without any disruption. You're using Excel, you just continue and use it. You just see the results are faster. You use Tableau, same difference. >> So we had all the vertical rock stars in here. So we had Colin in yesterday, we had Stonebraker around earlier. Andy Palmer just came on and Chris here with the CEO who ultimately sold the company to HP. That really didn't do anything with it and then spun it off and now it's back. Aaron was, he had a spring in his step yesterday. So when you think about, Vertica. The technology behind Vertica go back 10 years and where we come now give us a little journey of, your data journey. >> So I think it plays into the, the original assertion is that, vertical is a best-in-class platform for analytics but it was yet another platform. The analog I give now, is now we have Snowflake and six months, 12 months from now we're going to have another one. And that creates a set of problems if you have to live in the physical world. Because you've all these islands of data and I believe, it's about the data not about the models, it's about the data. You can't get optimal results if you don't have an optimal access to the pertinent data. I believe that having that Universal API is going to make the next platform that more valuable. You're not going to be making the trade-off is, okay we have this platform that has some neat capability but the trade-off is from an enterprise architecture perspective we're never going to be able to connect all this stuff. That's how all of these things proliferated. My view is, in a world where you have that single pane of glass, that abstraction layer between the user and the data. Then innovation can be spawned quicker and you can use these tools effectively 'cause you're not compromising being able to get a logical view of the data and get access to it as a user. >> What's your issue with Snowflake you mentioned them, Mugli's company-- >> No issue, they're a great partner of ours. We eliminate the friction between the user going from an on-prem solution to the cloud. >> Slootman just took over there. So you know where that's going. >> Yep (laughing) >> Frank's got the magic touch. Okay good, you say they're a partner yours how are you guys partnering? >> They refer us into customers that, if you want to buy Snowflake now the next issue is, how do i migrate? You don't. You put our virtualization layer in and then we allow you access to Snowflake in a non-disruptive way, versus having to move data into their system or into a particular cloud which creates sales friction. >> Moving data is just, you want to avoid it at all cost. >> I do want to ask you because I met with your predecessors, Dave Mariani last year and I know he was kind of a reluctant CEO he didn't really want to be CEO but wanted to be CTO, which is what he is now. How did that come about, that they found you that you connected with them and decided this was the right opportunity. >> That's a great question. I actually looked at the company at the seed stage when I was in venture, but I had this thing as you know that, I wanted to move companies to Boston and they're about my vintage age-wise and he's married with four kids so that wasn't in the cards. I said look, it doesn't make sense for me to seed this company 'cause I can't give you the time you're out in California everything I'm instrumenting is around Boston. We parted friends. And I was skeptical whether he could build this 'cause people have been talking about building a heterogeneous universal semantic layer, for years and it's never come to fruition. And then he read in Fortune or Forbes that I was leaving Accomplice and that I was looking for one more company to operate. He reached out and he told me what they were doing that hey, we really built it but we need help and I don't want to run this. It's not right for the company and the opportunity So he said, "I'll come and I'll consult to you." I put together a plan and I had my Vertica and data robot. NekTony guys do the technical diligence to make sure that the architecture wasn't wedded to the dupe, like all the other ones were and when I saw it wasn't then I knew the market opportunity was to take that, rifle and point it at that legacy $150 billion BI market not at the billion dollar market of Hadoop. And when we did that, we've been growing at 162% quarter-over-quarter. We've built development centers in Bulgaria. We've moved all operations, non-technical to Boston here down in our South Station. We've been on fire and we are the partner of choice of every cloud manner, because we eliminate the sales friction, for customers being able to take advantage of movement to the cloud and we're able through our intelligent pipeline and capability. We're able to reduce the cost significantly of queries because we understand and we were able to intelligently cash those queries. >> Sales ops is here, all-- >> Sales marketing, customer support, customer success and we're building a machine learning team here at Dev team here. >> Where are you in that sort of Boston build-out? >> We have an office on 711 Atlantic that we opened in the fall. We're actually moving from 4,000 square feet to 10,000 this month. In less than six months and we'll house by the first year, 100 employees in Boston 100 in Bulgaria and about that same hundred in San Mateo. >> Are you going after net new business mainly? Or there's a lot of legacy BI out there are you more displacing those products? >> A couple of things. What we find is that, customers want to evolve into the cloud, they don't want a revolution they want a evolution. So we allow them, because we support hybrid cloud to keep some data behind the firewall and then experiment with moving other data to the cloud platform of choice but we're still providing that one logical view. I would say most of our customers are looking to reap platform, off of Teradata or something onto a, another platform like Snowflake. And then we have a set of customers that see that as part of the solution but not the whole solution. They're more true hybrids but I would say that 80% of our customers are traditional BI customers that are trying to contemporize their environments and be able to take advantage of tabular support and multidimensional, the things that we do in addition to the cube world. >> They can keep whatever they're using. >> Correct, that's the key. >> Did you do the series D, you did, right? >> Yes, Morgan Stanely led. >> So you're not actively but you're good for now, It was like $50 million >> Yeah we raised $50 million. >> You're good for a bit. Who's in the Chris Lynch target? (laughs) Who's the enemy? Vertica, I could say it was the traditional database guys. Who's the? >> We're in a unique position, we're almost Switzerland so we could be friend to foe, of anybody in that ecosystem because we can, non-disruptively re-platform customers between legacy platforms or from legacy platforms to the cloud. We're an interesting position. >> So similar to the file sharing. File virtualization company >> The Copier. >> Copier yeah. >> It puts us in an interesting position. They need to be friends with us and at the same time I'm sure that they're concerned about the capabilities we have but we have a number of retail customers for instance that have asked us to move down from Amazon to Google BigQuery, which we accommodate and because we can do that non-disruptively. The cost and the ability to move is eliminated. It gives customers true freedom of choice. >> How worried are you, that AWS tries to replicate what you guys do. You're in their sights. >> I think there are technical, legal and structural barriers to them doing that. The technical is, this team has been at it for six and a half years. So to do what we do, they'll have to do what we've done. Structurally from a business perspective if they could, I'm not sure they want to. The way to think about Amazon is, they're no different than Teradata, except for they want the same vendor lock-in except they want it to be the Amazon Cloud when Teradata wanted it to be, their data warehouse. >> They don't promote multi-cloud versus-- >> Yeah, they don't want multi-cloud they don't want >> On Prem >> Customers to have a freedom of choice. Would they really enable a heterogeneous abstraction layer, I don't think they would nor do I think any of the big guys would. They all claim to have this capability for their system. It's like the old IBM adage I'm in prison but the food's going to get three squares a day, I get cable TV but I'm in prison. (laughing) >> Awesome, all right, parting thoughts. >> Parting thoughts, oh geez you got to give me a question I'm not that creative. >> What's next, for you guys? What should we be paying attention to? >> I think you're going to see some significant announcements in September regarding the company and relationships that I think will validate the impact we're having in the market. >> Give you some leverage >> Yeah, will give us, better channel leverage. We have a major technical announcement that I think will be significant to the marketplace and what will be highly disruptive to some of the people you just mentioned. In terms of really raising the bar for customers to be able to have the freedom of choice without any sort of vendor lock-in. And I think that that will create some counter strike which we'll be ready for. (laughing) >> If you've never heard of AtScale before trust me you're going to in the next 18 months. Chris Lynch, thanks so much for coming on theCUBE. >> It's my pleasure. >> Great to see you. All right, keep it right there everybody we're back with our next guest, right after this short break you're watching theCUBE from MIT, right back. (upbeat music)

>> from Cambridge, Massachusetts. It's three Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back. We're here to wrap up the M I T. Chief data officer officer, information quality. It's hashtag m i t CDO conference. You're watching the Cube. I'm David Dante, and Paul Gill is my co host. This is two days of coverage. We're wrapping up eyes. Our analysis of what's going on here, Paul, Let me let me kick it off. When we first started here, we talked about that are open. It was way saw the chief data officer role emerged from the back office, the information quality role. When in 2013 the CEO's that we talked to when we asked them what was their scope. We heard things like, Oh, it's very wide. Involves analytics, data science. Some CEOs even said Oh, yes, security is actually part of our purview because all the cyber data so very, very wide scope. Even in some cases, some of the digital initiatives were sort of being claimed. The studios were staking their claim. The reality was the CDO also emerged out of highly regulated industries financialservices healthcare government. And it really was this kind of wonky back office role. And so that's what my compliance, that's what it's become again. We're seeing that CEOs largely you're not involved in a lot of the emerging. Aye, aye initiatives. That's what we heard, sort of anecdotally talking to various folks At the same time. I feel as though the CDO role has been more fossilized than it was before. We used to ask, Is this role going to be around anymore? We had C I. Ose tell us that the CEO Rose was going to disappear, so you had both ends of the spectrum. But I feel as though that whatever it's called CDO Data's our chief analytics off officer, head of data, you know, analytics and governance. That role is here to stay, at least for for a fair amount of time and increasingly, issues of privacy and governance. And at least the periphery of security are gonna be supported by that CD a role. So that's kind of takeaway Number one. Let me get your thoughts. >> I think there's a maturity process going on here. What we saw really in 2016 through 2018 was, ah, sort of a celebration of the arrival of the CDO. And we're here, you know, we've got we've got power now we've got an agenda. And that was I mean, that was a natural outcome of all this growth and 90% of organizations putting sea Dios in place. I think what you're seeing now is a realization that Oh, my God, this is a mess. You know what I heard? This year was a lot less of this sort of crowing about the ascendance of sea Dios and Maura about We've got a big integration problem of big data cleansing problem, and we've got to get our hands down to the nitty gritty. And when you talk about, as you said, we had in here so much this year about strategic initiatives, about about artificial intelligence, about getting involved in digital business or customer experience transformation. What we heard this year was about cleaning up data, finding the data that you've got organizing it, applying meditator, too. It is getting in shape to do something with it. There's nothing wrong with that. I just think it's part of the natural maturation process. Organizations now have to go through Tiu to the dirty process of cleaning up this data before they can get to the next stage, which was a couple of three years out for most of >> the second. Big theme, of course. We heard this from the former head of analytics. That G s K on the opening keynote is the traditional methods have failed the the Enterprise Data Warehouse, and we've actually studied this a lot. You know, my analogy is often you snake swallowing a basketball, having to build cubes. E D W practitioners would always used to call it chasing the chips until we come up with a new chip. Oh, we need that because we gotta run faster because it's taking us hours and hours, weeks days to run these analytics. So that really was not an agile. It was a rear view mirror looking thing. And Sarbanes Oxley saved the E. D. W. Business because reporting became part of compliance thing perspective. The master data management piece we've heard. Do you consistently? We heard Mike Stone Breaker, who's obviously a technology visionary, was right on. It doesn't scale through this notion of duping. Everything just doesn't work and manually creating rules. It's just it's just not the right approach. This we also heard the top down data data enterprise data model doesn't works too complicated, can operationalize it. So what they do, they kick the can to governance. The Duke was kind of a sidecar, their big data that failed to live up to its promises. And so it's It's a big question as to whether or not a I will bring that level of automation we heard from KPMG. Certainly, Mike Stone breaker again said way heard this, uh, a cz well, from Andy Palmer. They're using technology toe automate and scale that big number one data science problem, which is? They spend all their time wrangling data. We'll see if that if that actually lives up >> to his probable is something we did here today from several of our guests. Was about the promise of machine learning to automate this day to clean up process and as ah Mark Ramsay kick off the conference saying that all of these efforts to standardize data have failed in the past. This does look, He then showed how how G s K had used some of the tools that were represented here using machine learning to actually clean up the data at G S. K. So there is. And I heard today a lot of optimism from the people we talked to about the capability of Chris, for example, talking about the capability of machine learning to bring some order to solve this scale scale problem Because really organizing data creating enterprise data models is a scale problem, and the only way you can solve that it's with with automation, Mike Stone breaker is right on top of that. So there was optimism at this event. There was kind of an ooh, kind of, ah, a dismay at seeing all the data problems they have to clean up, but also promised that tools are on the way that could do that. >> Yeah, The reason I'm an optimist about this role is because data such a hard problem. And while there is a feeling of wow, this is really a challenge. There's a lot of smart people here who are up for the challenge and have the d n a for it. So the role, that whole 360 thing. We talked about the traditional methods, you know, kind of failing, and in the third piece that touched on, which is really bringing machine intelligence to the table. We haven't heard that as much at this event. It's now front and center. It's just another example of a I injecting itself into virtually every aspect every corner of the industry. And again, I often jokes. Same wine, new bottle. Our industry has a habit of doing that, but it's cyclical, but it is. But we seem to be making consistent progress. >> And the machine learning, I thought was interesting. Several very guest spoke to machine learning being applied to the plumbing projects right now to cleaning up data. Those are really self contained projects. You can manage those you can. You can determine out test outcomes. You can vet the quality of the of the algorithms. It's not like you're putting machine learning out there in front of the customer where it could potentially do some real damage. There. They're vetting their burning in machine, learning in a environment that they control. >> Right, So So, Amy, Two solid days here. I think that this this conference has really grown when we first started here is about 130 people, I think. And now it was 500 registrants. This'd year. I think 600 is the sort of the goal for next year. Moving venues. The Cube has been covering this all but one year since 2013. Hope to continue to do that. Paul was great working with you. Um, always great work. I hope we can, uh we could do more together. We heard the verdict is bringing back its conference. You put that together. So we had column. Mahoney, um, had the vertical rock stars on which was fun. Com Mahoney, Mike Stone breaker uh, Andy Palmer and Chris Lynch all kind of weighed in, which was great to get their perspectives kind of the days of MPP and how that's evolved improving on traditional relational database. And and now you're Stone breaker. Applying all these m i. Same thing with that scale with Chris Lynch. So it's fun to tow. Watch those guys all Boston based East Coast folks some news. We just saw the news hit President Trump holding up jet icon contractors is we've talked about. We've been following that story very closely and I've got some concerns over that. It's I think it's largely because he doesn't like Bezos in The Washington Post Post. Exactly. You know, here's this you know, America first. The Pentagon says they need this to be competitive with China >> and a I. >> There's maybe some you know, where there's smoke. There's fire there, so >> it's more important to stick in >> the eye. That's what it seems like. So we're watching that story very closely. I think it's I think it's a bad move for the executive branch to be involved in those type of decisions. But you know what I know? Well, anyway, Paul awesome working with you guys. Thanks. And to appreciate you flying out, Sal. Good job, Alex Mike. Great. Already wrapping up. So thank you for watching. Go to silicon angle dot com for all the news. Youtube dot com slash silicon angles where we house our playlist. But the cube dot net is the main site where we have all the events. It will show you what's coming up next. We've got a bunch of stuff going on straight through the summer. And then, of course, VM World is the big kickoff for the fall season. Goto wicked bond dot com for all the research. We're out. Thanks for watching Dave. A lot day for Paul Gillon will see you next time.

>> From Cambridge, Massachusetts, it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts everybody, you're watching The Cube, the leader in tech coverage. My name is Dave Vellante here with my cohost Paul Gillin. This is day one of our two day coverage of the MIT CDOIQ conferences. CDO, Chief Data Officer, IQ, information quality. Colin Mahoney is here, he's a good friend and long time CUBE alum. I haven't seen you in awhile, >> I know >> But thank you so much for taking some time, you're like a special guest here >> Thank you, yeah it's great to be here, thank you. >> Yeah, so, this is not, you know, something that you would normally attend. I caught up with you, invited you in. This conference has started as, like back office governance, information quality, kind of wonky stuff, hidden. And then when the big data meme took off, kind of around the time we met. The Chief Data Officer role emerged, the whole Hadoop thing exploded, and then this conference kind of got bigger and bigger and bigger. Still intimate, but very high level, very senior. It's kind of come full circle as we've been saying, you know, information quality still matters. You have been in this data business forever, so I wanted to invite you in just to get your perspectives, we'll talk about what's new with what's going on in your company, but let's go back a little bit. When we first met and even before, you saw it coming, you kind of invested your whole career into data. So, take us back 10 years, I mean it was so different, remember it was Batch, it was Hadoop, but it was cool. There was a lot of cool >> It's still cool. (laughs) projects going on, and it's still cool. But, take a look back. >> Yeah, so it's changed a lot, look, I got into it a while ago, I've always loved data, I had no idea, the explosion and the three V's of data that we've seen over the last decade. But, data's really important, and it's just going to get more and more important. But as I look back I think what's really changed, and even if you just go back a decade I mean, there's an insatiable appetite for data. And that is not slowing down, it hasn't slowed down at all, and I think everybody wants that perfect solution that they can ask any question and get an immediate answers to. We went through the Hadoop boom, I'd argue that we're going through the Hadoop bust, but what people actually want is still the same. You know, they want real answers, accurate answers, they want them quickly, and they want it against all their information and all their data. And I think that Hadoop evolved a lot as well, you know, it started as one thing 10 years ago, with MapReduce and I think in the end what it's really been about is disrupting the storage market. But if you really look at what's disrupting storage right now, public clouds, S3, right? That's the new data league. So there's always a lot of hype cycles, everybody talks about you know, now it's Cloud, everything, for maybe the last 10 years it was a lot of Hadoop, but at the end of the day I think what people want to do with data is still very much the same. And a lot of companies are still struggling with it, hence the role for Chief Data Officers to really figure out how do I monetize data on the one hand and how to I protect that asset on the other hand. >> Well so, and the cool this is, so this conference is not a tech conference, really. And we love tech, we love talking about this, this is why I love having you on. We kind of have a little Vertica thread that I've created here, so Colin essentially, is the current CEO of Vertica, I know that's not your title, you're GM and Senior Vice President, but you're running Vertica. So, Michael Stonebreaker's coming on tomorrow, >> Yeah, excellent. >> Chris Lynch is coming on tomorrow, >> Oh, great, yeah. >> we've got Andy Palmer >> Awesome, yeah. >> coming up as well. >> Pretty cool. (laughs) >> So we have this connection, why is that important? It's because, you know, Vertica is a very cool company and is all about data, and it was all about disrupting, sort of the traditional relational database. It's kind of doing more with data, and if you go back to the roots of Vertica, it was like how do you do things faster? How do you really take advantage of data to really drive new business? And that's kind of what it's all about. And the tech behind it is really cool, we did your conference for many, many years. >> It's coming back by the way. >> Is it? >> Yeah, this March, so March 30th. >> Oh, wow, mark that down. >> At Boston, at the new Encore Hotel. >> Well we better have theCUBE there, bro. (laughs) >> Yeah, that's great. And yeah, you've done that conference >> Yep. >> haven't you before? So very cool customers, kind of leading edge, so I want to get to some of that, but let's talk the disruption for a minute. So you guys started with the whole architecture, MPP and so forth. And you talked about Cloud, Cloud really disrupted Hadoop. What are some of the other technology disruptions that you're seeing in the market space? >> I think, I mean, you know, it's hard not to talk about AI machine learning, and what one means versus the other, who knows right? But I think one thing that is definitely happening is people are leveraging the volumes of data and they're trying to use all the processing power and storage power that we have to do things that humans either are too expensive to do or simply can't do at the same speed and scale. And so, I think we're going through a renaissance where a lot more is being automated, certainly on the Vertica roadmap, and our path has always been initially to get the data in and then we want the platform to do a lot more for our customers, lots more analytics, lots more machine-learning in the platform. So that's definitely been a lot of the buzz around, but what's really funny is when you talk to a lot of customers they're still struggling with just some basic stuff. Forget about the predictive thing, first you've got to get to what happened in the past. Let's give accurate reporting on what's actually happening. The other big thing I think as a disruption is, I think IOT, for all the hype that it's getting it's very real. And every device is kicking off lots of information, the feedback loop of AB testing or quality testing for predictive maintenance, it's happening almost instantly. And so you're getting massive amounts of new data coming in, it's all this machine sensor type data, you got to figure out what it means really quick, and then you actually have to do something and act on it within seconds. And that's a whole new area for so many people. It's not their traditional enterprise data network warehouse and you know, back to you comment on Stonebreaker, he got a lot of this right from the beginning, you know, and I think he looked at the architectures, he took a lot of the best in class designs, we didn't necessarily invent everything, but we put a lot of that together. And then I think the other you've got to do is constantly re-invent your platform. We came out with our Eon Mode to run cloud native, we just got rated the best cloud data warehouse from a net promoter score rating perspective, so, but we got to keep going you know, we got to keep re-inventing ourselves, but leverage everything that we've done in the past as well. >> So one of the things that you said, which is kind of relevant for here, Paul, is you're still seeing a real data quality issue that customers are wrestling with, and that's a big theme here, isn't it? >> Absolutely, and the, what goes around comes around, as Dave said earlier, we're still talking about information quality 13 years after this conference began. Have the tools to improve quality improved all that much? >> I think the tools have improved, I think that's another area where machine learning, if you look at Tamr, and I know you're going to have Andy here tomorrow, they're leveraging a lot of the augmented things you can do with the processing to make it better. But I think one thing that makes the problem worse now, is it's gotten really easy to pour data in. It's gotten really easy to store data without having to have the right structure, the right quality, you know, 10 years ago, 20 years ago, everything was perfect before it got into the platform. Right, everything was, there was quality, everything was there. What's been happening over the last decade is you're pumping data into these systems, nobody knows if it's redundant data, nobody knows if the quality's any good, and the amount of data is massive. >> And it's cheap to store >> Very cheap to store. >> So people keep pumping it in. >> But I think that creates a lot of issues when it comes to data quality. So, I do think the technology's gotten better, I think there's a lot of companies that are doing a great job with it, but I think the challenge has definitely upped. >> So, go ahead. >> I'm sorry. You mentioned earlier that we're seeing the death of Hadoop, but I'd like you to elaborate on that becuase (Dave laughs) Hadoop actually came up this morning in the keynote, it's part of what GlaxoSmithKline did. Came up in a conversation I had with the CEO of Experian last week, I mean, it's still out there, why do you think it's in decline? >> I think, I mean first of all if you look at the Hadoop vendors that are out there, they've all been struggling. I mean some of them are shutting down, two of them have merged and they've got killed lately. I think there are some very successful implementations of Hadoop. I think Hadoop as a storage environment is wonderful, I think you can process a lot of data on Hadoop, but the problem with Hadoop is it became the panacea that was going to solve all things data. It was going to be the database, it was going to be the data warehouse, it was going to do everything. >> That's usually the kiss of death, isn't it? >> It's the kiss of death. And it, you know, the killer app on Hadoop, ironically, became SQL. I mean, SQL's the killer app on Hadoop. If you want to SQL engine, you don't need Hadoop. But what we did was, in the beginning Mike sort of made fun of it, Stonebreaker, and joked a lot about he's heard of MapReduce, it's called Group By, (Dave laughs) and that created a lot of tension between the early Vertica and Hadoop. I think, in the end, we embraced it. We sit next to Hadoop, we sit on top of Hadoop, we sit behind it, we sit in front of it, it's there. But I think what the reality check of the industry has been, certainly by the business folks in these companies is it has not fulfilled all the promises, it has not fulfilled a fraction on the promises that they bet on, and so they need to figure those things out. So I don't think it's going to go away completely, but I think its best success has been disrupting the storage market, and I think there's some much larger disruptions of technologies that frankly are better than HTFS to do that. >> And the Cloud was a gamechanger >> And a lot of them are in the cloud. >> Which is ironic, 'cause you know, cloud era, (Colin laughs) they didn't really have a cloud strategy, neither did Hortonworks, neither did MapR and, it just so happened Amazon had one, Google had one, and Microsoft has one, so, it's just convenient to-- >> Well, how is that affecting your business? We've seen this massive migration to the cloud (mumbles) >> It's actually been great for us, so one of the things about Vertica is we run everywhere, and we made a decision a while ago, we had our own data warehouse as a service offering. It might have been ahead of its time, never really took off, what we did instead is we pivoted and we say "you know what? "We're going to invest in that experience "so it's a SaaS-like experience, "but we're going to let our customers "have full control over the cloud. "And if they want to go to Amazon they can, "if they want to go to Google they can, "if they want to go to Azure they can." And we really invested in that and that experience. We're up on the Amazon marketplace, we have lots of customers running up on Amazon Cloud as well as Google and Azure now, and then about two years ago we went down and did this endeavor to completely re-architect our product so that we could separate compute and storage so that our customers could actually take advantage of the cloud economics as well. That's been huge for us, >> So you scale independent-- >> Scale independently, cloud native, add compute, take away compute, and for our existing customers, they're loving the hybrid aspect, they love that they can still run on Premise, they love that they can run up on a public cloud, they love that they can run in both places. So we will continue to invest a lot in that. And it is really, really important, and frankly, I think cloud has helped Vertica a lot, because being able to provision hardware quickly, being able to tie in to these public clouds, into our customers' accounts, give them control, has been great and we're going to continue on that path. >> Because Vertica's an ISV, I mean you're a software company. >> We're a software company. >> I know you were a part of HP for a while, and HP wanted to mash that in and run it on it's hardware, but software runs great in the cloud. And then to you it's another hardware platform. >> It's another hardware platform, exactly. >> So give us the update on Micro Focus, Micro Focus acquired Vertica as part of the HPE software business, how many years ago now? Two years ago? >> Less than two years ago. >> Okay, so how's that going, >> It's going great. >> Give us the update there. >> Yeah, so first of all it is great, HPE and HP were wonderful to Vertica, but it's great being part of a software company. Micro Focus is a software company. And more than just a software company it's a company that has a lot of experience bridging the old and the new. Leveraging all of the investments that you've made but also thinking about cloud and all these other things that are coming down the pike. I think for Vertica it's been really great because, as you've seen Vertica has gotten its identity back again. And that's something that Micro Focus is very good at. You can look at what Micro Focus did with SUSE, the Linux company, which actually you know, now just recently spun out of Micro Focus but, letting organizations like Vertica that have this culture, have this product, have this passion, really focus on our market and our customers and doing the right thing by them has been just really great for us and operating as a software company. The other nice thing is that we do integrate with a lot of other products, some of which came from the HPE side, some of which came from Micro Focus, security products is an example. The other really nice thing is we've been doing this insource thing at Micro Focus where we open up our source code to some of the other teams in Micro Focus and they've been contributing now in amazing ways to the product. In ways that we would just never be able to scale, but with 4,000 engineers strong in Micro Focus, we've got a much larger development organization that can actually contribute to the things that Vertica needs to do. And as we go into the cloud and as we do a lot more operational aspects, the experience that these teams have has been incredible, and security's another great example there. So overall it's been great, we've had four different owners of Vertica, our job is to continue what we do on the innovation side in the culture, but so far Micro Focus has been terrific. >> Well, I'd like to say, you're kind of getting that mojo back, because you guys as an independent company were doing your own thing, and then you did for a while inside of HP, >> We did. >> And that obviously changed, 'cause they wanted more integration, but, and Micro Focus, they know what they're doing, they know how to do acquisitions, they've been very successful. >> It's a very well run company, operationally. >> The SUSE piece was really interesting, spinning that out, because now RHEL is part of IBM, so now you've got SUSE as the lone independent. >> Yeah. >> Yeah. >> But I want to ask you, go back to a technology question, is NoSQL the next Hadoop? Are these databases, it seems to be that the hot fad now is NoSQL, it can do anything. Is the promise overblown? >> I think, I mean NoSQL has been out almost as long as Hadoop, and I, we always say not only SQL, right? Mike's said this from day one, best tool for the job. Nothing is going to do every job well, so I think that there are, whether it's key value stores or other types of NoSQL engines, document DB's, now you have some of these DB's that are running on different chips, >> Graph, yeah. >> there's always, yeah, graph DBs, there's always going to be specialty things. I think one of the things about our analytic platform is we can do, time series is a great example. Vertica's a great time series database. We can compete with specialized time series databases. But we also offer a lot of, the other things that you can do with Vertica that you wouldn't be able to do on a database like that. So, I always think there's going to be specialty products, I also think some of these can do a lot more workloads than you might think, but I don't see as much around the NoSQL movement as say I did a few years ago. >> But so, and you mentioned the cloud before as kind of, your position on it I think is a tailwind, not to put words in your mouth, >> Yeah, yeah, it's a great tailwind. >> You're in the Amazon marketplace, I mean they have products that are competitive, right? >> They do, they do. >> But, so how are you differentiating there? >> I think the way we differentiate, whether it's Redshift from Amazon, or BigQuery from Google, or even what Azure DB does is, first of all, Vertica, I think from, feature functionality and performance standpoint is ahead. Number one, I think the second thing, and we hear this from a lot of customers, especially at the C-level is they don't want to be locked into these full stacks of the clouds. Having the ability to take a product and run it across multiple clouds is a big thing, because the stack lock-in now, the full stack lock-in of these clouds is scary. It's really easy to develop in their ecosystems but you get very locked into them, and I think a lot of people are concerned about that. So that works really well for Vertica, but I think at the end of the day it's just, it's the robustness of the product, we continue to innovate, when you look at separating compute and storage, believe it or not, a lot of these cloud-native databases don't do that. And so we can actually leverage a lot of the cloud hardware better than the native cloud databases do themselves. So, like I said, we have to keep going, those guys aren't going to stop, and we actually have great relationships with those companies, we work really well with the clouds, they seem to care just as much about their cloud ecosystem as their own database products, and so I think that's going to continue as well. >> Well, Colin, congratulations on all the success >> Yeah, thank you, yeah. >> It's awesome to see you again and really appreciate you coming to >> Oh thank you, it's great, I appreciate the invite, >> MIT. >> it's great to be here. >> All right, keep it right there everybody, Paul and I will be back with our next guest from MIT, you're watching theCUBE. (electronic jingle)